### LangChain How-to Guide: Using Tools in a Chain

Source: https://python.langchain.com/docs/how_to/

Demonstrates how to integrate tools into LangChain chains, enabling LLMs to interact with external functionalities. This guide covers the setup and invocation of tools within a chain.

```python
# Example of using tools in a chain (conceptual)
from langchain_core.tools import tool
from langchain_core.runnables import RunnableSequence

@tool
def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"The weather in {city} is sunny."

# Assume an LLM is defined as 'llm'
# chain = RunnableSequence(llm, ...)
# result = chain.invoke({"input": "What's the weather like in London?"})

```

--------------------------------

### User Signup API Examples

Source: https://context7_llms

Demonstrates how to sign up a new user using the API. Includes examples for making the POST request via cURL, JavaScript (fetch API), and Python (requests library). All examples require a valid CAPTCHA response.

```bash
curl -X POST \
  https://api.rememberizer.ai/api/v1/auth/signup/ \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com",
    "password": "secure_password",
    "name": "John Doe",
    "captcha": "recaptcha_response"
  }'
```

```javascript
const signUp = async () => {
  const response = await fetch('https://api.rememberizer.ai/api/v1/auth/signup/', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      email: 'user@example.com',
      password: 'secure_password',
      name: 'John Doe',
      captcha: 'recaptcha_response'
    })
  });
  
  const data = await response.json();
  console.log(data);
};

signUp();
```

```python
import requests
import json

def sign_up():
    headers = {
        "Content-Type": "application/json"
    }
    
    payload = {
        "email": "user@example.com",
        "password": "secure_password",
        "name": "John Doe",
        "captcha": "recaptcha_response"
    }
    
    response = requests.post(
        "https://api.rememberizer.ai/api/v1/auth/signup/",
        headers=headers,
        data=json.dumps(payload)
    )
    
    data = response.json()
    print(data)

sign_up()
```

--------------------------------

### User Signup API Examples

Source: https://llm.rememberizer.ai/llms-full.txt

Demonstrates how to sign up a new user using the API. Includes examples for making the POST request via cURL, JavaScript (fetch API), and Python (requests library). All examples require a valid CAPTCHA response.

```bash
curl -X POST \
  https://api.rememberizer.ai/api/v1/auth/signup/ \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com",
    "password": "secure_password",
    "name": "John Doe",
    "captcha": "recaptcha_response"
  }'
```

```javascript
const signUp = async () => {
  const response = await fetch('https://api.rememberizer.ai/api/v1/auth/signup/', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      email: 'user@example.com',
      password: 'secure_password',
      name: 'John Doe',
      captcha: 'recaptcha_response'
    })
  });
  
  const data = await response.json();
  console.log(data);
};

signUp();
```

```python
import requests
import json

def sign_up():
    headers = {
        "Content-Type": "application/json"
    }
    
    payload = {
        "email": "user@example.com",
        "password": "secure_password",
        "name": "John Doe",
        "captcha": "recaptcha_response"
    }
    
    response = requests.post(
        "https://api.rememberizer.ai/api/v1/auth/signup/",
        headers=headers,
        data=json.dumps(payload)
    )
    
    data = response.json()
    print(data)

sign_up()
```

--------------------------------

### Installation Commands

Source: https://github.com/skydeckai/mcp-server-rememberizer

Commands to install the Rememberizer AI LLM tools via package managers.

```bash
npx @michaellatman/mcp-get@latest install mcp-server-rememberizer
```

```bash
npx -y @smithery/cli install mcp-server-rememberizer --client claude
```

--------------------------------

### User Sign In API Examples

Source: https://context7_llms

Demonstrates how to sign in an existing user using the API. Includes examples for making the POST request via cURL, JavaScript (fetch API), and Python (requests library). All examples require a valid CAPTCHA response.

```bash
curl -X POST \
  https://api.rememberizer.ai/api/v1/auth/signin/ \
  -H "Content-Type: application/json" \
  -d '{
    "login": "user@example.com",
    "password": "secure_password",
    "captcha": "recaptcha_response"
  }'
```

```javascript
const signIn = async () => {
  const response = await fetch('https://api.rememberizer.ai/api/v1/auth/signin/', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      login: 'user@example.com',
      password: 'secure_password',
      captcha: 'recaptcha_response'
    })
  });
  
  // Check for auth cookies in response
  if (response.status === 204) {
    console.log("Login successful!");
  } else {
    console.error("Login failed!");
  }
};

signIn();
```

```python
import requests
import json

def sign_in():
    headers = {
        "Content-Type": "application/json"
    }
    
    payload = {
        "login": "user@example.com",
        "password": "secure_password",
        "captcha": "recaptcha_response"
    }
    
    response = requests.post(
        "https://api.rememberizer.ai/api/v1/auth/signin/",
        headers=headers,
        data=json.dumps(payload)
    )
    
    if response.status_code == 204:
        print("Login successful!")
    else:
        print("Login failed!")

sign_in()
```

--------------------------------

### Install via Smithery CLI

Source: https://github.com/skydeckai/mcp-server-rememberizer

Installs the mcp-server-rememberizer using the Smithery CLI. This command specifies the server to install and the client to be used with it.

```shell
npx -y @smithery/cli install mcp-server-rememberizer --client claude
```

--------------------------------

### User Sign In API Examples

Source: https://llm.rememberizer.ai/llms-full.txt

Demonstrates how to sign in an existing user using the API. Includes examples for making the POST request via cURL, JavaScript (fetch API), and Python (requests library). All examples require a valid CAPTCHA response.

```bash
curl -X POST \
  https://api.rememberizer.ai/api/v1/auth/signin/ \
  -H "Content-Type: application/json" \
  -d '{
    "login": "user@example.com",
    "password": "secure_password",
    "captcha": "recaptcha_response"
  }'
```

```javascript
const signIn = async () => {
  const response = await fetch('https://api.rememberizer.ai/api/v1/auth/signin/', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      login: 'user@example.com',
      password: 'secure_password',
      captcha: 'recaptcha_response'
    })
  });
  
  // Check for auth cookies in response
  if (response.status === 204) {
    console.log("Login successful!");
  } else {
    console.error("Login failed!");
  }
};

signIn();
```

```python
import requests
import json

def sign_in():
    headers = {
        "Content-Type": "application/json"
    }
    
    payload = {
        "login": "user@example.com",
        "password": "secure_password",
        "captcha": "recaptcha_response"
    }
    
    response = requests.post(
        "https://api.rememberizer.ai/api/v1/auth/signin/",
        headers=headers,
        data=json.dumps(payload)
    )
    
    if response.status_code == 204:
        print("Login successful!")
    else:
        print("Login failed!")

sign_in()
```

--------------------------------

### Install Rememberizer MCP Server via mcp-get

Source: https://context7_llms

Installs the Rememberizer MCP Server package using the mcp-get command-line tool. This is one method for integrating AI assistants with Rememberizer's knowledge management.

```bash
npx @michaellatman/mcp-get@latest install mcp-server-rememberizer
```

--------------------------------

### Install Rememberizer MCP Server via mcp-get

Source: https://llm.rememberizer.ai/llms-full.txt

Installs the Rememberizer MCP Server package using the mcp-get command-line tool. This is one method for integrating AI assistants with Rememberizer's knowledge management.

```bash
npx @michaellatman/mcp-get@latest install mcp-server-rememberizer
```

--------------------------------

### Initial Hugging Face API Query Function

Source: https://huggingface.co/blog/getting-started-with-embeddings

This Python code snippet demonstrates an initial approach to querying a Hugging Face inference API for sentence similarity tasks. It sets up the model ID, API URL, authentication headers, and a `query` function that sends POST requests with a list of texts. The example includes sample input sentences and prints the API response.

```python
model_id = "sentence-transformers/all-MiniLM-L6-v2"
api_url = f"https://api-inference.huggingface.co/models/{model_id}"

hf_token = os.environ['access_token']
headers = {"Authorization": f"Bearer {hf_token}"}

def query(texts):
    response = requests.post(api_url, headers=headers, json={"inputs": texts, "options":{"wait_for_model":True}})
    return response.json()

texts = ["How do I get a replacement Medicare card?",
         "What is the monthly premium for Medicare Part B?",
         "How do I terminate my Medicare Part B (medical insurance)?"]

output = query(texts)
print(output)
```

--------------------------------

### MCP Quick Start Options

Source: https://modelcontextprotocol.io/introduction

Presents the primary starting paths for users engaging with the Model Context Protocol (MCP), catering to server developers, client developers, and end-users of applications like Claude Desktop.

```jsx
_jsx(CardGroup, {
 cols: 2,
 children: [
  _jsx(Card, {
   title: "For Server Developers",
   icon: "bolt",
   href: "/quickstart/server",
   children: _jsx(_components.p, {
    children: "Get started building your own server to use in Claude for Desktop and other\nclients"
   })
  }),
  _jsx(Card, {
   title: "For Client Developers",
   icon: "bolt",
   href: "/quickstart/client",
   children: _jsx(_components.p, {
    children: "Get started building your own client that can integrate with all MCP servers"
   })
  }),
  _jsx(Card, {
   title: "For Claude Desktop Users",
   icon: "bolt",
   href: "/quickstart/user",
   children: _jsx(_components.p, {
    children: "Get started using pre-built servers in Claude for Desktop"
   })
  })
 ]
})
```

--------------------------------

### OAuth 2.0 Authorization Request Example

Source: https://tools.ietf.org/html/rfc6749

An example HTTP GET request from a client to an authorization server's endpoint, including common parameters like response_type, client_id, redirect_uri, and state.

```HTTP
GET /authorize?response_type=code&client_id=s6BhdRkqt3&state=xyz
    &redirect_uri=https%3A%2F%2Fclient%2Eexample%2Ecom%2Fcb HTTP/1.1
Host: server.example.com
```

--------------------------------

### Setup Qdrant Client

Source: https://context7_llms

Configures the Qdrant client with connection details like URL and API key. This is the initial step to interact with a Qdrant database.

```Python
from qdrant_client import QdrantClient

QDRANT_URL = "http://localhost:6333"  # or your Qdrant cloud URL
QDRANT_API_KEY = "your_qdrant_api_key"  # if using Qdrant Cloud
QDRANT_COLLECTION_NAME = "your_collection"

qdrant_client = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY  # Only for Qdrant Cloud
)
```

--------------------------------

### Retrieve Document Information Examples

Source: https://llm.rememberizer.ai/llms-full.txt

Demonstrates how to fetch document details using the Rememberizer AI API. Examples are provided for cURL, JavaScript, and Python, showing how to make the GET request and include the necessary API key and IDs.

```bash
curl -X GET \
  https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/1234 \
  -H "x-api-key: YOUR_API_KEY"
```

```javascript
const getDocumentInfo = async (vectorStoreId, documentId) => {
  const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/${documentId}`, {
    method: 'GET',
    headers: {
      'x-api-key': 'YOUR_API_KEY'
    }
  });
  
  const data = await response.json();
  console.log(data);
};

getDocumentInfo('vs_abc123', 1234);
```

```python
import requests

def get_document_info(vector_store_id, document_id):
    headers = {
        "x-api-key": "YOUR_API_KEY"
    }
    
    response = requests.get(
        f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/{document_id}",
        headers=headers
    )
    
    data = response.json()
    print(data)

get_document_info('vs_abc123', 1234)
```

--------------------------------

### Retrieve Document Information Examples

Source: https://context7_llms

Demonstrates how to fetch document details using the Rememberizer AI API. Examples are provided for cURL, JavaScript, and Python, showing how to make the GET request and include the necessary API key and IDs.

```bash
curl -X GET \
  https://api.rememberizer.ai/api/v1/vector-stores/vs_abc123/documents/1234 \
  -H "x-api-key: YOUR_API_KEY"
```

```javascript
const getDocumentInfo = async (vectorStoreId, documentId) => {
  const response = await fetch(`https://api.rememberizer.ai/api/v1/vector-stores/${vectorStoreId}/documents/${documentId}`, {
    method: 'GET',
    headers: {
      'x-api-key': 'YOUR_API_KEY'
    }
  });
  
  const data = await response.json();
  console.log(data);
};

getDocumentInfo('vs_abc123', 1234);
```

```python
import requests

def get_document_info(vector_store_id, document_id):
    headers = {
        "x-api-key": "YOUR_API_KEY"
    }
    
    response = requests.get(
        f"https://api.rememberizer.ai/api/v1/vector-stores/{vector_store_id}/documents/{document_id}",
        headers=headers
    )
    
    data = response.json()
    print(data)

get_document_info('vs_abc123', 1234)
```

--------------------------------

### Open in Colab Badge

Source: https://huggingface.co/blog/getting-started-with-embeddings

An image link that allows users to open the associated tutorial notebook directly in Google Colaboratory.

```html
<a href="https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/80_getting_started_with_embeddings.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
```

--------------------------------

### Setup Qdrant Client

Source: https://llm.rememberizer.ai/llms-full.txt

Configures the Qdrant client with connection details like URL and API key. This is the initial step to interact with a Qdrant database.

```Python
from qdrant_client import QdrantClient

QDRANT_URL = "http://localhost:6333"  # or your Qdrant cloud URL
QDRANT_API_KEY = "your_qdrant_api_key"  # if using Qdrant Cloud
QDRANT_COLLECTION_NAME = "your_collection"

qdrant_client = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY  # Only for Qdrant Cloud
)
```

--------------------------------

### MCP Examples and Integrations

Source: https://modelcontextprotocol.io/introduction

Provides links to explore official MCP server implementations and a list of clients that support MCP integrations, encouraging users to discover and utilize the MCP ecosystem.

```jsx
_jsx(CardGroup, {
 cols: 2,
 children: [
  _jsx(Card, {
   title: "Example Servers",
   icon: "grid",
   href: "/examples",
   children: _jsx(_components.p, {
    children: "Check out our gallery of official MCP servers and implementations"
   })
  }),
  _jsx(Card, {
   title: "Example Clients",
   icon: "cubes",
   href: "/clients",
   children: _jsx(_components.p, {
    children: "View the list of clients that support MCP integrations"
   })
  })
 ]
})
```

--------------------------------

### Rememberizer AI API Request (curl)

Source: https://docs.rememberizer.ai/developer/authorizing-rememberizer-apps

Command-line example using curl to make a GET request to the /api/me/ endpoint with the required Authorization header.

```shell
curl -H "Authorization: Bearer OAUTH-TOKEN" https://api.rememberizer.ai/api/me/
```

--------------------------------

### Prepare Text Data for Embedding

Source: https://huggingface.co/blog/getting-started-with-embeddings

Defines a list of text strings to be processed by an embedding model. This is the initial input for generating vector representations of the text.

```python
texts = ["How do I get a replacement Medicare card?",
        "What is the monthly premium for Medicare Part B?",
        "How do I terminate my Medicare Part B (medical insurance)?",
        "How do I sign up for Medicare?",
        "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
        "How do I sign up for Medicare Part B if I already have Part A?",
        "What are Medicare late enrollment penalties?",
        "What is Medicare and who can get it?",
        "How can I get help with my Medicare Part A and Part B premiums?",
        "What are the different parts of Medicare?",
        "Will my Medicare premiums be higher because of my higher income?",
        "What is TRICARE ?",
        "Should I sign up for Medicare Part B if I have Veterans' Benefits?"]

output = query(texts)
```

--------------------------------

### MCP Tutorials and Tools

Source: https://modelcontextprotocol.io/introduction

Offers guidance through tutorials on building MCP with LLMs and debugging, alongside access to essential tools like the MCP Inspector for testing and debugging MCP servers.

```jsx
_jsx(CardGroup, {
 cols: 2,
 children: [
  _jsx(Card, {
   title: "Building MCP with LLMs",
   icon: "comments",
   href: "/tutorials/building-mcp-with-llms",
   children: _jsx(_components.p, {
    children: "Learn how to use LLMs like Claude to speed up your MCP development"
   })
  }),
  _jsx(Card, {
   title: "Debugging Guide",
   icon: "bug",
   href: "/legacy/tools/debugging",
   children: _jsx(_components.p, {
    children: "Learn how to effectively debug MCP servers and integrations"
   })
  }),
  _jsx(Card, {
   title: "MCP Inspector",
   icon: "magnifying-glass",
   href: "/legacy/tools/inspector",
   children: _jsx(_components.p, {
    children: "Test and inspect your MCP servers with our interactive debugging tool"
   })
  }),
  _jsx(Card, {
   title: "MCP Workshop (Video, 2hr)",
   icon: "person-chalkboard",
   href: "https://www.youtube.com/watch?v=kQmXtrmQ5Zg",
   children: _jsx("iframe", {
    src: "https://www.youtube.com/embed/kQmXtrmQ5Zg"
   })
  })
 ]
})
```

--------------------------------

### Access Rememberizer API with Access Token

Source: https://docs.rememberizer.ai/developer/authorizing-rememberizer-apps

Demonstrates how to authenticate with an access token and make a GET request to the /api/me/ endpoint. Includes an example using curl.

```APIDOC
Endpoint: GET https://api.rememberizer.ai/api/me/

Description:
  Retrieves user information using an access token for authentication.

Authentication:
  Requires an 'Authorization' header with a Bearer token.

Headers:
  Authorization: Bearer OAUTH-TOKEN

Example Usage (curl):
  curl -H "Authorization: Bearer OAUTH-TOKEN" https://api.rememberizer.ai/api/me/

Parameters:
  None directly in the URL, authentication is via header.

Returns:
  Typically returns user profile information upon successful authentication.
```

--------------------------------

### Theme Toggling and Analytics Initialization

Source: https://huggingface.co/blog/getting-started-with-embeddings

This snippet handles dark mode toggling based on user preferences or system settings and initializes the Plausible analytics script.

```javascript
const guestTheme = document.cookie.match(/theme=(\w+)/)?.["1"];
document.documentElement.classList.toggle('dark', guestTheme === 'dark' || ( (!guestTheme || guestTheme === 'system') && window.matchMedia('(prefers-color-scheme: dark)').matches));

window.plausible = window.plausible || function () { (window.plausible.q = window.plausible.q || []).push(arguments); };
```

--------------------------------

### Python Application Entrypoint

Source: https://github.com/skydeckai/rememberizer-integration-samples

The main application file for the 'talk-to-slack' integration, responsible for setting up the Flask web server and handling user interactions with Rememberizer and Slack.

```python
from flask import Flask, render_template, request, redirect, url_for
import os
import requests

app = Flask(__name__)

# Load environment variables
REMEMBERIZER_CLIENT_ID = os.environ.get('REMEMBERIZER_CLIENT_ID')
REMEMBERIZER_CLIENT_SECRET = os.environ.get('REMEMBERIZER_CLIENT_SECRET')
REMEMBERIZER_REDIRECT_URI = os.environ.get('REMEMBERIZER_REDIRECT_URI')
REMEMBERIZER_AUTH_URL = 'https://api.rememberizer.com/auth/authorize'
REMEMBERIZER_TOKEN_URL = 'https://api.rememberizer.com/auth/token'
REMEMBERIZER_SEARCH_URL = 'https://api.rememberizer.com/knowledge/search'

@app.route('/')
def index():
    if not REMEMBERIZER_CLIENT_ID or not REMEMBERIZER_REDIRECT_URI:
        return "Configuration error: Please set REMEMBERIZER_CLIENT_ID and REMEMBERIZER_REDIRECT_URI.", 500
    auth_url = f"{REMEMBERIZER_AUTH_URL}?client_id={REMEMBERIZER_CLIENT_ID}&redirect_uri={REMEMBERIZER_REDIRECT_URI}&response_type=code&scope=knowledge:read"
    return render_template('index.html', auth_url=auth_url)

@app.route('/callback')
def callback():
    code = request.args.get('code')
    if not code:
        return "Authorization failed.", 400

    if not REMEMBERIZER_CLIENT_ID or not REMEMBERIZER_CLIENT_SECRET or not REMEMBERIZER_REDIRECT_URI:
        return "Configuration error: Missing Rememberizer credentials.", 500

    token_payload = {
        'grant_type': 'authorization_code',
        'client_id': REMEMBERIZER_CLIENT_ID,
        'client_secret': REMEMBERIZER_CLIENT_SECRET,
        'code': code,
        'redirect_uri': REMEMBERIZER_REDIRECT_URI
    }

    try:
        token_response = requests.post(REMEMBERIZER_TOKEN_URL, json=token_payload)
        token_response.raise_for_status() # Raise an exception for bad status codes
        token_data = token_response.json()
        access_token = token_data.get('access_token')

        if not access_token:
            return "Failed to obtain access token.", 500

        # Store token securely (e.g., in session or database)
        # For simplicity, we'll just pass it to the next step
        return redirect(url_for('search_interface', token=access_token))

    except requests.exceptions.RequestException as e:
        return f"Error obtaining token: {e}", 500

@app.route('/search', methods=['GET', 'POST'])
def search_interface():
    access_token = request.args.get('token') or request.form.get('token')
    if not access_token:
        return "Access token not provided.", 400

    query = request.form.get('query')
    results = []
    if query:
        headers = {'Authorization': f'Bearer {access_token}'}
        params = {'query': query}
        try:
            search_response = requests.get(REMEMBERIZER_SEARCH_URL, headers=headers, params=params)
            search_response.raise_for_status()
            results = search_response.json()
        except requests.exceptions.RequestException as e:
            return f"Error searching knowledge base: {e}", 500

    return render_template('chatbox.html', results=results, query=query, token=access_token)

if __name__ == '__main__':
    port = int(os.environ.get('PORT', 5000))
    app.run(host='0.0.0.0', port=port, debug=True)
```

--------------------------------

### Convert Embeddings to Pandas DataFrame

Source: https://huggingface.co/blog/getting-started-with-embeddings

Converts the output from an embedding query into a Pandas DataFrame. This structure facilitates data manipulation and analysis, with each row representing an embedding.

```python
import pandas as pd

embeddings = pd.DataFrame(output)
```

--------------------------------

### Batch Upload to Vector Store (Ruby Setup)

Source: https://context7_llms

Illustrates the setup for a batch file upload to a Vector Store using Ruby. It includes necessary require statements for HTTP requests, URI parsing, JSON handling, and MIME type detection, along with parameter definitions for the upload process.

```Ruby
require 'net/http'
require 'uri'
require 'json'
require 'mime/types'

# Upload files to a Vector Store in batches
#
# @param vector_store_id [String] ID of the Vector Store
# @param folder_path [String] Path to folder containing files to upload
# @param batch_size [Integer] Number of files to upload in each batch
# @param file_types [Array<String>] Optional array of file extensions to filter by
# @param delay_between_batches [Float] Seconds to wait between batches
```

--------------------------------

### Export Embeddings to CSV

Source: https://huggingface.co/blog/getting-started-with-embeddings

Saves the Pandas DataFrame containing embeddings to a CSV file named 'embeddings.csv'. This format is easily loadable by libraries like Hugging Face Datasets.

```python
embeddings.to_csv("embeddings.csv", index=False)
```

--------------------------------

### Hugging Face Hub Configuration

Source: https://huggingface.co/blog/getting-started-with-embeddings

Configuration object for Hugging Face Hub, including feature flags, API URLs, and keys for various services like SSH, Captcha, and DocSearch.

```json
{
  "features": {
    "signupDisabled": false
  },
  "sshGitUrl": "git@hf.co",
  "moonHttpUrl": "https:\/\/huggingface.co",
  "captchaApiKey": "bd5f2066-93dc-4bdd-a64b-a24646ca3859",
  "captchaDisabledOnSignup": true,
  "datasetViewerPublicUrl": "https:\/\/datasets-server.huggingface.co",
  "stripePublicKey": "pk_live_x2tdjFXBCvXo2FFmMybezpeM00J6gPCAAc",
  "environment": "production",
  "userAgent": "HuggingFace (production)",
  "spacesIframeDomain": "hf.space",
  "spacesApiUrl": "https:\/\/api.hf.space",
  "docSearchKey": "ece5e02e57300e17d152c08056145326e90c4bff3dd07d7d1ae40cf1c8d39cb6",
  "logoDev": {
    "apiUrl": "https:\/\/img.logo.dev\/",
    "apiKey": "pk_UHS2HZOeRnaSOdDp7jbd5w"
  }
}
```

--------------------------------

### Display Top Similar FAQs

Source: https://huggingface.co/blog/getting-started-with-embeddings

Retrieves and prints the actual text of the most similar FAQs identified by the semantic search. It uses the `corpus_id` from the search results to index into a list of original FAQ texts.

```python
# Assume 'texts' is a list containing the original FAQ strings
# For example: texts = ["FAQ 1 text", "FAQ 2 text", ...]

# Extract and print the top 5 most similar FAQ texts
print([texts[hits[0][i]['corpus_id']] for i in range(len(hits[0]))])
```

--------------------------------

### Python Application Entrypoint

Source: https://github.com/skydeckai/rememberizer

The main application file for the 'talk-to-slack' integration, responsible for setting up the Flask web server and handling user interactions with Rememberizer and Slack.

```python
from flask import Flask, render_template, request, redirect, url_for
import os
import requests

app = Flask(__name__)

# Load environment variables
REMEMBERIZER_CLIENT_ID = os.environ.get('REMEMBERIZER_CLIENT_ID')
REMEMBERIZER_CLIENT_SECRET = os.environ.get('REMEMBERIZER_CLIENT_SECRET')
REMEMBERIZER_REDIRECT_URI = os.environ.get('REMEMBERIZER_REDIRECT_URI')
REMEMBERIZER_AUTH_URL = 'https://api.rememberizer.com/auth/authorize'
REMEMBERIZER_TOKEN_URL = 'https://api.rememberizer.com/auth/token'
REMEMBERIZER_SEARCH_URL = 'https://api.rememberizer.com/knowledge/search'

@app.route('/')
def index():
    if not REMEMBERIZER_CLIENT_ID or not REMEMBERIZER_REDIRECT_URI:
        return "Configuration error: Please set REMEMBERIZER_CLIENT_ID and REMEMBERIZER_REDIRECT_URI.", 500
    auth_url = f"{REMEMBERIZER_AUTH_URL}?client_id={REMEMBERIZER_CLIENT_ID}&redirect_uri={REMEMBERIZER_REDIRECT_URI}&response_type=code&scope=knowledge:read"
    return render_template('index.html', auth_url=auth_url)

@app.route('/callback')
def callback():
    code = request.args.get('code')
    if not code:
        return "Authorization failed.", 400

    if not REMEMBERIZER_CLIENT_ID or not REMEMBERIZER_CLIENT_SECRET or not REMEMBERIZER_REDIRECT_URI:
        return "Configuration error: Missing Rememberizer credentials.", 500

    token_payload = {
        'grant_type': 'authorization_code',
        'client_id': REMEMBERIZER_CLIENT_ID,
        'client_secret': REMEMBERIZER_CLIENT_SECRET,
        'code': code,
        'redirect_uri': REMEMBERIZER_REDIRECT_URI
    }

    try:
        token_response = requests.post(REMEMBERIZER_TOKEN_URL, json=token_payload)
        token_response.raise_for_status() # Raise an exception for bad status codes
        token_data = token_response.json()
        access_token = token_data.get('access_token')

        if not access_token:
            return "Failed to obtain access token.", 500

        # Store token securely (e.g., in session or database)
        # For simplicity, we'll just pass it to the next step
        return redirect(url_for('search_interface', token=access_token))

    except requests.exceptions.RequestException as e:
        return f"Error obtaining token: {e}", 500

@app.route('/search', methods=['GET', 'POST'])
def search_interface():
    access_token = request.args.get('token') or request.form.get('token')
    if not access_token:
        return "Access token not provided.", 400

    query = request.form.get('query')
    results = []
    if query:
        headers = {'Authorization': f'Bearer {access_token}'}
        params = {'query': query}
        try:
            search_response = requests.get(REMEMBERIZER_SEARCH_URL, headers=headers, params=params)
            search_response.raise_for_status()
            results = search_response.json()
        except requests.exceptions.RequestException as e:
            return f"Error searching knowledge base: {e}", 500

    return render_template('chatbox.html', results=results, query=query, token=access_token)

if __name__ == '__main__':
    port = int(os.environ.get('PORT', 5000))
    app.run(host='0.0.0.0', port=port, debug=True)
```

--------------------------------

### Load Embedded Dataset and Convert to PyTorch

Source: https://huggingface.co/blog/getting-started-with-embeddings

Loads an embedded dataset from the Hugging Face Hub using the `datasets` library and converts it into a PyTorch `FloatTensor` for efficient numerical operations. This prepares the dataset for similarity comparisons.

```python
from datasets import load_dataset
import torch

# Load the embedded dataset from the Hub
faqs_embeddings = load_dataset('namespace/repo_name')

# Convert the dataset embeddings to a PyTorch FloatTensor
dataset_embeddings = torch.from_numpy(faqs_embeddings["train"].to_pandas().to_numpy()).to(torch.float)
```

--------------------------------

### Repository Structure Overview

Source: https://github.com/skydeckai/rememberizer-integration-samples

Displays the directory structure of the Rememberizer integration samples repository, outlining the organization of documentation, GPT integration examples, notebooks, and a Slack integration.

```tree
rememberizer-integration-samples/
├── docs_for_ai/
│ ├── Authorizing Rememberizer apps.md
│ └── developer_guide.md
├── gpt/
│ ├── README.md
│ └── rememberizer_openapi.yml
├── notebooks/
│ ├── .env.sample
│ ├── callback_server.py
│ └── developer_guide.ipynb
├── talk-to-slack/
│ ├── .env.sample
│ ├── README
│ ├── app.py
│ ├── requirements.txt
│ ├── static/
│ │ └── styles.css
│ ├── templates/
│ │ ├── answer.html
│ │ ├── chatbox.html
│ │ ├── dashboard.html
│ │ ├── error.html
│ │ ├── index.html
│ │ └── slack_info.html
│ └── test_app.py
```

--------------------------------

### Hugging Face Accelerated Inference API

Source: https://huggingface.co/blog/getting-started-with-embeddings

Information about the Hugging Face Accelerated Inference API, which is recommended for embedding multiple texts or images. It allows for faster inference and offers options for CPU or GPU usage.

```apidoc
HuggingFaceAcceleratedInferenceAPI:
  Description: Provides accelerated inference for models on Hugging Face.
  Use Cases:
    - Embedding multiple texts or images.
    - Faster inference compared to standard API.
  Features:
    - Choice between CPU or GPU usage.
  Rate Limiting: The API does not enforce strict rate limitations but favors steady request flows and balances loads evenly across resources.
  Documentation: https://huggingface.co/docs/api-inference/index
```

--------------------------------

### Setup Rememberizer Client

Source: https://context7_llms

Configures the Rememberizer client with API key, vector store ID, and base URL. This setup is necessary for uploading documents to Rememberizer.

```Python
import requests
import time

REMEMBERIZER_API_KEY = "your_rememberizer_api_key"
VECTOR_STORE_ID = "vs_abc123"  # Your Rememberizer vector store ID
BASE_URL = "https://api.rememberizer.ai/api/v1"

# Batch size for processing
BATCH_SIZE = 100
```

--------------------------------

### Hide Snap Pixel Iframes

Source: https://eng.snap.com/machine-learning-snap-ad-ranking

Hides iframes with names starting with 'snap' to prevent the Snap Pixel from displaying. This is a temporary override pending a proper fix in the Snap Pixel GTM setup.

```css
iframe[name^="snap"] {
  display: none;
}
```

--------------------------------

### Perform Semantic Search with Sentence Transformers

Source: https://huggingface.co/blog/getting-started-with-embeddings

Utilizes the `semantic_search` function from the `sentence-transformers` library to find the most similar items in a dataset to a given query embedding. It uses cosine similarity by default and returns the top-k most relevant results.

```python
from sentence_transformers.util import semantic_search

# Perform semantic search to find the top 5 most similar FAQs
hits = semantic_search(query_embeddings, dataset_embeddings, top_k=5)
```

--------------------------------

### Running the Flask Application

Source: https://context7_llms

Command to start the Flask development server and the callback URL format for configuring external services.

```shell
flask run
# Access the app at http://localhost:5000
# Callback URL: https://<YOURHOST>/auth/rememberizer/callback
```

--------------------------------

### Hugging Face Hub Dataset Upload (UI)

Source: https://huggingface.co/blog/getting-started-with-embeddings

Instructions for uploading a dataset file (e.g., embeddings.csv) to the Hugging Face Hub using the web interface. This involves creating a new dataset, adding files, and committing changes.

```apidoc
HuggingFaceHubDatasetUpload:
  Steps:
    1. Navigate to Hugging Face Hub website.
    2. Click on user profile in the top right corner.
    3. Select "New dataset."
    4. Choose Owner (organization or individual), dataset name, and license.
    5. Select privacy (private or public) and create the dataset.
    6. Go to the "Files" tab.
    7. Click "Add file" and then "Upload file."
    8. Drag and drop or upload the dataset file (e.g., embeddings.csv).
    9. Commit the changes to upload the file.
```